- End-to-end training platform: design and build the onprem ML platform covering the full training lifecycle.
- Model optimization & edge deployment:pruning,quantization, and deploymen to edge devices for real-time LiDAR applications.
- Self-service for MLengineers:build platform capabilities that let ML engineers deploy independently, working cross-functionally with ML, Data, and DevOps.
- On-prem LLM hosting:own the infrastructure that backs our internal AI capabilities
- Agentic workflow infrastructure: partner with AI workflow engineers to translate requirements into scalable platform capabilities supporting the agentic toolswith on-prem LLMs
- Model lifecycle management: manage multiple open-weight models, fine-tuning pipelines, and versioned rollouts from experiment to production.